Grant: Discovery Grants Program - Individual

Recent progress in computer science and engineering has introduced new computing paradigms such as the Internet of Things (IoT) and Ambient Intelligence (AmI). The core idea in these emerging technologies relies on distributed physical interaction with environments, in which low-cost computational nodes are ubiquitously connected within an infrastructure to collect and analyze data locally in real-time and favorable in terms of power consumption, communication overhead, security, and privacy. In this context, utilizing Artificial Intelligence (AI) and Machine Learning (ML) algorithms, which mostly come with significant computational complexity, would be highly challenging and requires immense improvements in computational efficiency at both the algorithmic and hardware level. Deep learning (DL), as a subset of ML, is in the heart of various artificial intelligence applications. Current DL applications are based on Artificial Neural Networks (ANN) consisting of neurons with continuous activation functions and a set of continuous-time weighted inputs. Although these networks are extremely powerful, they are also very resource hungry, which makes it very challenging to deploy them on edge computing nodes. Unlike ANNs, Spiking Neural Networks (SNN), use "asynchronous" and discrete-time spiking "events" to code, compute and transmit information. Consistent with biological behavior of Central Nervous System (CNS), these individual spikes are sparse in time and have a uniform amplitude, thus have the capability to carry information content by encoding data in the spike rate and/or timing. Due to the nature of spiking data coding and processing, SNNs are capable to be more hardware friendly and energy-efficient than ANNs and are thus very appealing for resource-restricted processors. Despite their rich mathematical and biological background, implementation and training deep SNNs remains a major challenge. Recently, a few promising methods have been developed to implement low-cost binarized ANN and spike-like outputs as well as training deep SNNs, which offer acceptable accuracy compared with lower hardware implementation cost. This research explores a tightly interwoven Algorithm-Hardware codesign techniques for low-cost spiking neuro-engines and follows implementation driven algorithmic innovations, together with customized yet flexible processing architectures, that can be utilized in ambient intelligent applications. The main goal of the research is based on the design and implementation of application-specific neuro-engines for embedded systems as a cross-disciplinary work on machine learning, bio-inspired computing, pervasive computing, and hardware design and optimization. Working with SpiNNaker group [1], as a postdoc fellow researcher, and several years of academic research in the field of bio-inspired computing has been a great source of inspiration to the applicant to conduct his research.